Abstract
The present work evaluates four medical image retrieval approaches based on features derived from image miniatures. We argue that due to the restricted domain of medical image data, the standardized acquisition protocols and the absence of a potentially cluttered background a holistic image description is sufficient to capture high-level image similarities. We compare four different miniature 2D and 3D descriptors and corresponding metrics, in terms of their retrieval performance: (A) plain miniatures together with euclidean distances in a k Nearest Neighbor based retrieval backed by kD-trees; (B) correlations of rigidly aligned miniatures, initialized using the kD-tree; (C) distribution fields together with the l 1-norm; (D) SIFT-like histogram of gradients using the χ 2-distance. We evaluate the approaches on two data sets: the ImageClef 2009 benchmark of 2D radiographs with the aim to categorize the images and a large set of 3D-CTs representing a realistic sample in a hospital PACS with the objective to estimate the location of the query volume.
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Donner, R., Haas, S., Burner, A., Holzer, M., Bischof, H., Langs, G. (2012). Evaluation of Fast 2D and 3D Medical Image Retrieval Approaches Based on Image Miniatures. In: Müller, H., Greenspan, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2011. Lecture Notes in Computer Science, vol 7075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28460-1_12
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DOI: https://doi.org/10.1007/978-3-642-28460-1_12
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